Within the world of business intelligence (BI) there is always much philosophical debate on what information architects refer to as “context.” However, while the debates of “one version of truth” versus “multiple perspectives” continue, we forget to realize the influence of context already embedded within the data.
Context can be the invisible lineage within data and not necessarily the data itself. The simple act of profiling or analyzing data in database tables has already inherited context. When you look through rows in a customer table in the data warehouse or rows of metrics in a fact table in a data mart, what do you see? That customer table has already organized data elements within the context of a provided definition of a customer. Perhaps that definition was derived from another operational systems’ definition of the context needed to process transactions associated with customers. And, perhaps the ancestors of that context were requirements for business processes from various business functions.
Often the data we work with every day is created, captured, transformed, and stored by this method of evolution, carrying with it a predefined definition of “context.” The best data modelers can see beyond this inherited context, rediscover the truth of what is being modeled, and provide access to data within a well-defined, consistent context. This article discusses the value of structured and unstructured data, and how we can bridge the gap between using both metadata and modern BI architectures to unite data and context, realizing the full potential of our BI.